Parameter Estimation Research Articles

Abundance-mediated species interactions.

Species interactions shape biodiversity patterns, community assemblage, and the dynamics of wildlife populations. Ecological theory posits that the strength of interspecific interactions is fundamentally underpinned by the population sizes of the involved species. Nonetheless, prevalent approaches for modeling species interactions predominantly center around occupancy states. Here, we use simulations to illuminate the inadequacies of modeling species interactions solely as a function of occupancy, as is common practice in ecology. We demonstrate erroneous inference into species interactions due to error in parameter estimates when considering species occupancy alone. To address this critical issue, we propose, develop, and demonstrate an abundance-mediated interaction framework designed explicitly for modeling species interactions involving two or more species from detection/non-detection data. We present Markov chain Monte Carlo (MCMC) samplers tailored for diverse ecological scenarios, including intraguild predation, disease- or predator-mediated competition, and trophic cascades. Illustrating the practical implications of our approach, we compare inference from modeling the interactions in a three-species network involving coyotes (Canis latrans), fishers (Pekania pennanti), and American marten (Martes americana) in North America as a function of occupancy states and as a function of abundance. When modeling interactions as a function of abundance rather than occupancy, we uncover previously unidentified interactions. Our study emphasizes that accounting for abundance-mediated interactions rather than simple co-occurrence patterns can fundamentally alter our comprehension of system dynamics. Through an empirical case study and comprehensive simulations, we demonstrate the importance of accounting for abundance when modeling species interactions, and we present a statistical framework equipped with MCMC samplers to achieve this paradigm shift in ecological research.

Exploring the probiotic potential of yeast strains isolated from ripe banana and plantain peels: in vitro and in vivo assessment.

Probiotics have been shown to improve host access to vitamins, generate vitamins and improve gastrointestinal tract functions throughout transit yeasts which are indicated to potentially function as probiotics with some health benefits. The aim of this study was to evaluate the probiotic potential of yeast isolates from banana and plantain peels. Yeast strains were isolated from ripe banana and plantain peels, using the standard procedures. Isolates that showed negativity for pathogenicity assays were used for in vitro and in vivo probiotic screening. For the in vivo assay, 30 male Wistar rats, divided into six treatment groups (five rats per treatment), were used. At the expiration of administration, the rats were euthanized for the estimation of yeast translocation and haematological parameters. A total of 16 yeasts were isolated, out of which, five (Hanseniaspora uvarum, Hanseniaspora lachancei, Hanseniaspora opuntiae and two species of Kodamaea ohmeri) isolates that showed negative results for pathogenicity assays were used for in vitro and in vivo assays. The study revealed the growth of the test yeast strains at the different incubation temperatures (25-45°C) and pH (4-9). However, significantly highest growths were observed at pH 4 and 5 and NaCl concentration of 4%. All the isolates showed remarkable growth at the bile concentrations used. In addition, the strains showed resistance to the antibiotics and antimycotics at the concentration used for investigation. In vivo assay results showed no significant differences between weight gain, haematological parameters and behavioural scores between control and treatment groups of the rats. The ability of the yeasts to show tolerance to stress conditions and lack of negative impacts to haematological, behavioural and yeast translocations are indications of their probiotic capabilities.

A Novel Self-Supervised Learning-Based Method for Dynamic CT Brain Perfusion Imaging.

Dynamic computed tomography (CT)-based brain perfusion imaging is a non-invasive technique that can provide quantitative measurements of cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT). However, due to high radiation dose, dynamic CT scan with a low tube voltage and current protocol is commonly used. Because of this reason, the increased noise degrades the quality and reliability of perfusion maps. In this study, we aim to propose and investigate the feasibility of utilizing a convolutional neural network and a bi-directional long short-term memory model with an attention mechanism to self-supervisedly yield the impulse residue function (IRF) from dynamic CT images. Then, the predicted IRF can be used to compute the perfusion parameters. We evaluated the performance of the proposed method using both simulated and real brain perfusion data and compared the results with those obtained from two existing methods: singular value decomposition and tensor total-variation. The simulation results showed that the overall performance of parameter estimation obtained from the proposed method was superior to that obtained from the other two methods. The experimental results showed that the perfusion maps calculated from the three studied methods were visually similar, but small and significant differences in perfusion parameters between the proposed method and the other two methods were found. We also observed that there were several low-CBF and low-CBV lesions (i.e., suspected infarct core) found by all comparing methods, but only the proposed method revealed longer MTT. The proposed method has the potential to self-supervisedly yield reliable perfusion maps from dynamic CT images.

Joint extented Kalman filter-based parameter estimation in networked SEIR models with stochastic variability: analysis using COVID-19 daily data from southern India

Graph Laplacian diffusion is utilised to augment the susceptible-infected-removed (SEIR) epidemic model by integrating a weighted network and introducing variability in the transmission rate parameter to simulate population movement across network nodes. Employing the Joint Extended Kalman filter (JEKF), our objective is to derive three critical parameters: the transmission rate (β), recovery rate (γ), and latent period rate (α). The JEKF's iterative approach continuously anticipates and updates the system state based on current parameter estimations, making it adaptable for parameter estimation in dynamic systems. Integration of model predictions with observed data further enhances accuracy. Subsequently, we offer visual insights and demonstrate our methodology through experiments conducted on a simple tree network and a small-world Watts-Strogatz graph. Our ultimate aim is to precisely determine optimal parameter values to formulate effective COVID-19 management plans tailored for South India.

ZTF SN Ia DR2: Study of Type Ia supernova light-curve fits

Type Ia supernova (SN Ia) cosmology relies on the estimation of light-curve parameters to derive precision distances, which are used to infer cosmological parameters such as $H_0$, $ and $w$. The empirical SALT2 light-curve modeling that relies on only two parameters, a stretch $x_1$ and a color $c$, has been used by the community for almost two decades. We study the ability of the SALT2 model to fit the nearly 3000 cosmology-grade SN Ia light curves from the second release of the Zwicky Transient Facility (ZTF) cosmology science working group. While the ZTF data were not used to train SALT2, the algorithm models the ZTF SN Ia optical light curves remarkably well, except for light-curve points prior to $-10$ d from maximum, where the training critically lacks data. We find that the light-curve fitting is robust against the considered choice of phase range, but we show that the $ $ d range is optimal in terms of statistics and accuracy. We do not detect any significant features in the light-curve fit residuals that could be connected to the host environment. Potential systematic uncertainties associated tp population differences related to the SN Ia host properties might thus not be accountable for by the inclusion of addition of light-curve parameters. However, a small but significant inconsistency between residuals of blue and red SN Ia strongly suggests the existence of a phase-dependent color term, with potential implications for the use of SNe Ia in precision cosmology. We thus encourage further work in this area to explore this possibility, and we emphasize that SN Ia cosmology must include a SALT2 retraining to accurately model the light curves and avoid biasing the derivation of cosmological parameters.

Inferences of accelerated generalized type-I hybrid censoring data under power hazard rate population

In reliability engineering or medical studies, more information about the life expectancy of products or materials is needed. This information under use conditions is more difficult to collect especially under highly reliable case. In this paper, the problem of statistical inference when the lifetime of a product or material has the power failure rate distribution is developed. To save the cost and time induced by units the experiment is designed with respect to partially step-stress accelerated life tests (ALTs) with type-I generalized hybrid censoring scheme (GHCS). The point estimate of the model parameters as well as the accelerated factor are obtained with respect to the maximum likelihood (ML) and Bayes methods. Also, the asymptotic normal properties of ML estimates, two bootstrap techniques and Markov Chain Monte Carlo techniques are used to formulate interval estimators. A real lifetime data set is used to illustrate the application of the proposed model. Finally, a Monte Carlo simulation study is constructed to discuss and assess, the proposed model and estimation methods.

Inferring redshift and galaxy properties via a multi-task neural net with probabilistic outputs. An application to simulated MOONS spectra

The era of large-scale astronomical surveys demands innovative approaches for rapid and accurate analysis of extensive spectral data, and a promising direction in which to address this challenge is offered by machine learning. Here, we introduce a new pipeline M-TOPnet (Multi-Task network Outputting Probabilities), which employs a convolutional neural network with residual learning to simultaneously derive redshift and other key physical properties of galaxies from their spectra. Our tool efficiently encodes spectral information into a latent space, employing distinct downstream branches for each physical quantity, thereby benefiting from multi-task learning. Notably, our method handles the redshift output as a probability distribution, allowing for a more refined and robust estimation of this critical parameter. We demonstrate preliminary results using simulated data from the MOONS instrument, which will soon be operating at the ESO/VLT. We highlight the effectiveness of our tool in accurately predicting the redshift, stellar mass, and star formation rate of galaxies at $z 1-3$, even for faint sources ($m_H 24$) for which traditional methods often struggle. Through analysis of the output probability distributions, we demonstrate that our pipeline enables robust quality screening of the results, achieving accuracy rates of up to 99<!PCT!> in redshift determination (defined as predictions within $| z| < 0.01$ relative to the true redshift) with $8 h$ exposure spectra, while automatically identifying potentially problematic cases. Our pipeline thus emerges as a powerful solution for the upcoming challenges in observational astronomy, combining precision, interpretability, and efficiency, all aspects that are crucial for analysing the massive datasets expected from next-generation instruments.

Identification and application of dynamic parameters of manipulator based on improved IRLS algorithm

Purpose To overcome the shortcomings of traditional dynamic parameter identification methods in accuracy and efficiency, this paper aims to propose a dynamic parameter identification method based on improved iterative reweighted least squares (IIRLS) algorithm. Design/methodology/approach First, Newton–Euler method is used to establish the dynamic model of the robot, which is linearized and reorganized. Then, taking the improved Fourier series as the excitation trajectory, the optimization model with objective function is established and optimized. Then, the manipulator runs the optimized trajectory and collects the running state of the joint. Finally, the iterative process of iterative reweighted least squares (IRLS) algorithm is improved by combining clustering algorithm and matrix inversion operation rules, and the dynamic model of robot is identified by using the processed collected data. Findings Experimental results show that, compared with the IRLS algorithm, the root mean square of the proposed IIRLS algorithm is reduced by 4.18% and the identification time is reduced by 94.92% when the sampling point is 1001. This shows that IIRLS algorithm can identify the dynamic model more accurately and efficiently. Originality/value It effectively solves the problem of low accuracy and efficiency of parameter identification in robot dynamic model and can be used as an effective method for parameter estimation of robot dynamic model, which is of great significance to the research of control method based on robot model.

Hybrid Brown-Bear and Hippopotamus Algorithms with Fractional Order Chaos Maps for Precise Solar PV Model Parameter Estimation

The rise in photovoltaic (PV) energy utilization has led to increased research on its functioning, as its accurate modeling is crucial for system simulations. However, capturing nonlinear current–voltage traits is challenging due to limited data from cells’ datasheets. This paper presents a novel enhanced version of the Brown-Bear Optimization Algorithm (EBOA) for determining the ideal parameters for the circuit model. The presented EBOA incorporates several modifications aimed at improving its searching capabilities. It combines Fractional-order Chaos maps (FC maps), which support the BOA settings to be adjusted in an adaptive manner. Additionally, it integrates key mechanisms from the Hippopotamus Optimization (HO) to strengthen the algorithm’s exploitation potential by leveraging surrounding knowledge for more effective position updates while also improving the balance between global and local search processes. The EBOA was subjected to extensive mathematical validation through the application of benchmark functions to rigorously assess its performance. Also, PV parameter estimation was achieved by combining the EBOA with a Newton–Raphson approach. Numerous module and cell varieties, including RTC France, STP6-120/36, and Photowatt-PWP201, were assessed using double-diode and single-diode PV models. The higher performance of the EBOA was shown by a statistical comparison with many well-known metaheuristic techniques. To illustrate this, the root mean-squared error values achieved by our scheme using (SDM, DDM) for RTC France, STP6-120/36, and PWP201 are as follows: (8.183847 × 10−4, 7.478488 × 10−4), (1.430320 × 10−2, 1.427010 × 10−2), and (2.220075 × 10−3, 2.061273 × 10−3), respectively. The experimental results show that the EBOA works better than alternative techniques in terms of accuracy, consistency, and convergence.

Synergistic Impact of Active Case Detection and Early Hospitalization for Controlling the Spread of Yellow Fever Outbreak in Nigeria: An Epidemiological Modeling and Optimal Control Analysis

Capturing the factors influencing yellow fever (YF) outbreaks is essential for effective public health interventions, especially in regions like Nigeria, where the disease poses significant health risks. This study explores the synergistic effects of active case detection (ACD) and early hospitalization on controlling YF transmission dynamics. We develop a dynamic model that integrates vaccination, active case detection, and hospitalization to enhance our understanding of disease spread and inform prevention strategies. Our methodology encompasses mechanistic dynamic modeling, optimal control analysis, parameter estimation, model fitting, and sensitivity analyses to study YF transmission dynamics, ensuring the robustness of control measures. We employ advanced mathematical techniques, including next-generation matrix methods, to accurately compute the reproduction number and assess outbreak transmissibility. Rigorous qualitative analysis of the model reveals two equilibria: disease-free and endemic, demonstrating global asymptotic stability and its impact on overall YF transmission dynamics, significantly affecting control and prevention mechanisms. Furthermore, through sensitivity analysis, we identify crucial parameters of the model that require urgent attention for more effective YF control. Moreover, our results highlight the critical roles of ACD and early hospitalization in reducing YF transmission. These insights provide a foundation for informed decision making and resource allocation in epidemic control efforts, ultimately contributing to the enhancement of public health strategies aimed at mitigating the impact of YF outbreaks.

Reliability Assessment for Dual Constant‐Stress Accelerated Life Test Based on Weibull Distribution With Type‐II Censoring

ABSTRACTAccelerated life test (ALT) is a generally feasible and effective approach for evaluating the reliability of highly reliable products at normal operating conditions. This paper considers the reliability assessment for a dual constant‐stress ALT with Type‐II censoring for the Weibull distribution with constant shape parameter and a log‐linear link between the scale parameter and the stress factors. We derive the maximum likelihood estimates, weighted least squares estimates using a certain random variable transformation, and a combined maximum likelihood‐least squares estimates for the unknown parameters. In addition, we construct confidence intervals for the parameters of interest using both asymptotic theory and parametric bootstrap technique. Finally, simulation studies and an illustrative example are presented to demonstrate the effectiveness and feasibility of the proposed methods.

Archetypal crop trait dynamics for enhanced retrieval of biophysical parameters from Sentinel-2 MSI

We present a new method for estimating biophysical parameters from Earth Observation (EO) data using a crop-specific empirical model based on the PROSAIL Radiative Transfer (RT) model, called an ‘archetype’ model. The first-order model presented uses maximum biophysical parameter magnitude, phenological and soil parameters to describe the spectral reflectance (400–2500 nm) of vegetation over time. The approach assumes smooth variation and archetypical coordination of crop biophysical parameters over time for a given crop. The form of coordination is learned from a large sample of observations. Using Sentinel-2 observations of maize from Northeast China in 2019, we map reflectance to biophysical parameters using an inverse model operator, synchronise the parameters to a consistent time frame using a double logistic model of LAI, then derive the model archetypes as the median value of the synchronised samples. We apply the model to estimate time series of biophysical parameters for different cereal crops using an ensemble framework with a weighted K-nearest neighbour solution, and validate the results with ground measurements of different crops collected near Munich, Germany in 2017 and 2018. The results show R values greater than 0.8 for leaf area index (LAI) and leaf brown pigment content (Cbrown), with an RMSE of 0.94 m2/m2 for LAI and 0.15 for Cbrown. The chlorophyll content (Cab) and canopy water content (CCw) were retrieved at a higher level of accuracy, with R values around 0.9 and an RMSE of 6.59μg/cm2 for Cab and 0.03 g/cm2 for CCw. Comparison of forward-modelled hyperspectral reflectance with independent ground measures shows that the retrieved parameters account for 90% of the variation in canopy reflectance, with an overall RMSE of around 0.05 in reflectance units. The retrievals for all terms are mostly within 1σ when measurement and prediction uncertainty are taken into account, except for some early and late season issues in leaf and canopy water due to the complexity of canopy structure and understory during these periods. The approach provides a new form of constraint for the simultaneous estimation of biophysical parameters from EO and greatly reduces the rank of the problem. It is suitable for monitoring crop conditions where biophysical parameters vary smoothly over time consistently with each archetype form. The approach can be refined for other canopy types and canopy representations and could provide strong constraints on expected smoothly-varying canopy features to aid in the interpretation of EO signals across different regions of the electromagnetic spectrum.

The odyssey of women in university education: stochastic growth models for studying access and graduation process

This research aims to explore the evolution of statistics related to the proportion of women in university student populations (focusing on bachelor’s or equivalent degree programs) over an extensive period, spanning from the founding of the first university until recent times. This is done by utilizing empirical insights derived from comprehensive data series, presented here for the first time and primarily encompassing Greece, UK, Canada and USA. Motivated by the distinctive properties of these series and their sigmoid pattern, we delve into a general family of stochastic growth models for data analysis. A notable contribution of this paper lies in offering a novel interpretation of this family within a competing cause scenario, similar to those used in the theory of event history analysis. We emphasize on key common elements within the two statistical domains of research, thereby highlighting potential future research directions; besides, the interpretability of model parameters stands out as an advantage compared to other approaches such as those based on regression model theory. For assessing the proposed parameter estimation methodology under realistic conditions a simulation study is also conducted. The application and results are aimed at making a significant contribution to research in higher and university education by presenting comprehensive sets of consistent data and statistical tools to capture their fundamental characteristics and trends. Additionally, they can serve as valuable resources for education policy-makers and human resources planners alike.

A Hybrid Parameters Estimation Approach for Power Consumption Modeling of Ground Mobile Robots With Unknown Payload

ABSTRACTTypically, batteries are the main power source for mobile robots, and the practical constraints in carrying them pose challenges in achieving efficiency, autonomy, and safety objectives in robotic missions. Moreover, dealing with battery degradation requires a precise understanding of energy consumers' behavior in the system. Therefore, establishing an energy consumption model has an important role in addressing these challenges. This study focuses on the modeling and estimation of power consumption in indoor ground mobile robots with unknown payloads. We investigate key components and parameters that influence the actuation energy consumption of ground mobile robots equipped with DC motors, particularly in the context of pure rolling without slipping. To estimate the actuation power consumption of ground mobile robots, we present a hybrid parameters estimation approach. This method includes an offline step for estimating the mass‐decoupled coefficients of a power model, followed by two online steps for estimating the robot's unknown payload and its actuation power consumption. Simulation and experimental results with a differential drive robot (Turtlebot3) validate the effectiveness of the proposed approach. Additionally, the performance of the proposed power consumption estimation method is compared with two other data‐driven models based on multivariate linear regression and multilayer perceptron neural networks. The results demonstrate that the proposed method provides improved power consumption estimation accuracy on the selected indicators compared to the others. We also emphasize the importance of balancing the accuracy of power consumption estimation with its associated costs in the context of the proposed approach. This extra cost includes the energy needed for additional data measurement and processing. Considering this trade‐off is crucial for the practical implementation and resource optimization in robotic systems.

Evaluating the Performances of Robust Logistic Regression Models in the Presence of Outliers

Logistic regression models are widely used in the field of medical and behavioral sciences. These models are used to describe the effect of explanatory variables on a binary response variable. The maximum likelihood estimator (MLE) is commonly used to estimate the parameters of logistic regression models due to its efficiency under a parametric model. However, evidence has shown that the MLE is highly sensitive to outlying observations which might affect the parameter estimates. Robust methods are put forward to rectify this problem. This paper investigated the robustness of GM-Mallows and GM-Schweppes as an alternative to the commonly used ordinary logistic regression model in the presence of outliers. The study used a Monte Carlos Simulation, by generating a logistic regression model with Five independent normally distributed covariates. 5% of outliers was contaminated to the data on sample sizes 50, 200 and 400 respectively. The results showed that the GM-Mallows estimator perform best across all metrics having the lowest AIC, BIC, MSE and MAE except for n=50. This suggests that the robust methods, especially GM-Mallows, provide more reliable estimates in the presence of outliers. The finding suggests that if there is presence of outliers’ GM-Mallows appears to be the top choice, where the GM-Schweppes offers a middle ground, providing some robustness with perhaps less extreme adjustments. The ordinary logistic regression might be preferred if simplicity and interpretability are prioritized, and there's confidence that outliers are not a significant issue in the data.

Fast Parameter Estimation of Linear Frequency Modulation Signals in Marine Environments Based on Gradient Optimization Strategy

Multi-buoy sonar systems achieve target localization by receiving broadband Linear Frequency Modulation signals emitted from the transmitter. Accurate estimations of the parameters of Linear Frequency Modulation signals significantly enhance the localization accuracy. Linear Frequency Modulation signals can be focused into the fractional domain through Fractional Fourier Transform, but this increases the computational complexity. In marine environments, the low signal-to-noise ratio and multipath effects degrade the parameter estimation accuracy further. To address these issues, this paper proposes a fast estimation algorithm based on the Fractional Fourier Transform and a Gradient Subtraction-Average-Based Optimizer. This algorithm leverages the impulsive characteristics of Linear Frequency Modulation signals after Fractional Fourier Transform transformation, using the Fractional Fourier Transform as the fitness function. The Gradient Subtraction-Average-Based Optimizer algorithm includes three enhancement strategies: chaotic mapping initialization, a Golden Sine Algorithm, and an adaptive t-distribution variational operator. The simulation results demonstrate that the Gradient Subtraction-Average-Based Optimizer algorithm improves the issues of low diversity in the search agents, imbalanced global and local search capabilities, and susceptibility to local optima. A comparative analysis and statistical testing confirm that under a low signal-to-noise ratio and multipath effect conditions, the Gradient Subtraction-Average-Based Optimizer algorithm not only ensures real-time parameter estimation but also improves the estimation accuracy. The results of the parameter estimation provide reliable data support for subsequent target localization.

Simulation of rapid sand filters to understand and design sequential iron and manganese removal using reactive transport modelling

Iron (Fe2+), manganese (Mn2+), and ammonium (NH4+) oxidation processes were studied in three single media and three dual media full-scale rapid sand filters (RSFs) using reactive transport modelling (RTM) in PHREEQC and parameter estimation using PEST. Here, we present the insights gained into the spatial distribution of Fe and Mn mineral coatings in RSFs and its influence on the oxidation sequence and rates. Fe2+ and Mn2+ oxidation predominantly occurred simultaneously in the RSFs, contrary to the expected sequential oxidation based on Gibbs free energy calculations. During backwashing, RSF grains become fully mixed, which initiates heterogeneous Mn2+ oxidation on Mn-coated grains that end up in the top layer. The resulting grains have a mixed Fe/Mn mineral coating, which is limiting heterogeneous Mn2+ oxidation due to the limited Mn mineral surface available. Mixed coatings did not seem to affect Fe2+ oxidation rates, instead oxidation rates were increasing at lower pH. We found that RSFs can be designed to spatially separate Fe2+ and Mn2+ oxidation, which results in optimal conditions for Mn2+ oxidation. The RSF needs to consist of two layers with varying density to inhibit mixing and complete Fe2+ oxidation should occur in the top layer. The developed RTM can be used to estimate the depth at which Fe2+ oxidation is complete, and thus the ideal intersection depth of the two layers. A novel perspective is provided on how mineral coating distribution in single and dual media filters influence removal rates and the sequence of oxidation, which contributes to the design of more efficient groundwater filters.

Truthful and privacy-preserving generalized linear models

This paper explores estimating Generalized Linear Models (GLMs) when agents are strategic and privacy-conscious. We aim to design mechanisms that encourage truthful reporting, protect privacy, and ensure outputs are close to the true parameters. Initially, we address models with sub-Gaussian covariates and heavy-tailed responses with finite fourth moments, proposing a novel private, closed-form estimator. Our mechanism features: (1) o(1)-joint differential privacy with high probability; (2) o(1n)-approximate Bayes Nash equilibrium for (1−o(1))-fraction of agents; (3) o(1) error in parameter estimation; (4) individual rationality for (1−o(1)) of agents; (5) o(1) payment budget. We then extend our approach to linear regression with heavy-tailed data, using an ℓ4-norm shrinkage operator to propose a similar estimator and payment scheme.

A novel approach using iterative g-function and chaotic cooperation search for accurate voltage calculation of double and triple diode solar cell models

The solar cells can be accurately represented via double and triple-diode models (DDM and TDM, respectively). In this paper, a novel iterative procedure based on applying the g-function is proposed for double and triple solar cell voltage calculation for the first time. The proposed iterative procedure is performed without any approximations, while its application overcomes numerical limitations when applying literature-accepted approaches based on the Lambert W function. Also, one notable advantage of the proposed procedure is its effectiveness with arbitrary starting point values. Second, in this paper, an original equation for calculating solar cells’ root mean square error (RMSE) voltage (RMSEU) in the DDM and TDM, which aids in parameter estimation, is proposed. Third, the paper introduces a new metaheuristic algorithm called the Chaotic Cooperation Search Algorithm (ChCSA) for estimating solar cell parameters. The proposed approach (iterative procedure for voltage-current representation as well as a novel algorithm) is tested on two well-known solar cells in the literature and on measured current-voltage characteristics for different weather conditions. Applying the proposed algorithm and the novel iterative procedure for DDM and TDM voltage calculations demonstrated the accuracy and efficiency of the proposed approach for solar cell parameter estimation. In order to achieve the appropriate accuracy, it was shown that only a few iterations of the proposed iterative procedure are needed for the majority of measurement points in both observed cells. Furthermore, the application of the proposed algorithm outperforms existing algorithms in solar cell parameter estimation. Moreover, with the well-known Photowatt-PWP 201 solar module, the application of the proposed algorithm enables the estimation of the parameters of the solar cell so that the RMSEU value is reduced by about 50 % compared to the results obtained for the parameters determined by numerous approaches from the literature. Therefore, the presented research offers a novel and original double and triple solar cell modeling perspective.

Lassa fever outbreaks, mathematical models, and disease parameters: a systematic review and meta-analysis

Understanding the epidemiological parameters and transmission dynamics of Lassa fever, a significant public health threat in west Africa caused by the rodent-borne Lassa virus, is crucial for informing evidence-based interventions and outbreak response strategies. Therefore, our study aimed to collate and enhance understanding of the key epidemiological parameters of Lassa fever. We conducted a systematic review, searching PubMed and Web of Science for peer-reviewed studies published from database inception up to June 13, 2024, to compile and analyse key epidemiological parameters, mathematical models, and outbreaks of Lassa fever. English-language, peer-reviewed, original research articles were included if they reported on Lassa fever outbreak sizes, transmission models, viral evolution, transmission, natural history, severity, seroprevalence, or risk factors. Non-peer-reviewed literature was excluded. Data were extracted by two independent individuals from published literature, focusing on seroprevalence, transmissibility, epidemiological delays, and disease severity. We performed a meta-analysis to calculate pooled estimates of case-fatality ratios (CFRs) and the delay from symptom onset to hospital admission. This study is registered with PROSPERO (identifier number CRD42023393345). The database search returned 5614 potentially relevant studies, and a further 16 studies were identified from backward citation chaining. After de-duplication and exclusion, 193 publications met our inclusion criteria and provided 440 relevant parameter estimates in total. Although Lassa virus is endemic in west Africa, the spatiotemporal coverage of general-population seroprevalence estimates (ranging from 2·6% [6/232] to 58·2% [103/177]) was poor, highlighting the spatial uncertainty in Lassa fever risk. Similarly, only four basic reproduction number estimates (ranging from 1·13 to 1·40) were available. We estimated a pooled total random effect CFR of 33·5% (95% CI 25·8-42·2, I2=95%) and found potential variation in severity by geographical regions typically associated with specific Lassa virus lineages. We estimated a pooled total random effect mean symptom-onset-to-hospital-admission delay of 8·19 days (95% CI 7·31-9·06, I2=93%), but other epidemiological delays were poorly characterised in the existing literature. Our findings highlight the absence of empirical Lassa fever parameter estimates despite its high burden in west Africa. Improved surveillance approaches to capture mild cases in humans and to further cover rodent populations are needed to better understand Lassa fever transmission dynamics. Addressing these gaps is essential for developing accurate mathematical models and informing evidence-based interventions to mitigate the effect of Lassa fever on public health in endemic regions. UK Medical Research Council, National Institute for Health and Care Research, Academy of Medical Sciences, Wellcome, UK Department for Business, Energy, and Industrial Strategy, British Heart Foundation, Diabetes UK, Schmidt Foundation, Community Jameel, Royal Society, and Imperial College London. For the French translation of the abstract see Supplementary Materials section.